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This article aims to summarize the employers' requirements of scientific data specialists and the status quo of LIS education organizations' training system for scientific data specialists. It also focuses on the matching analysis between the course content and the responsibilities as well as requirements of scientific data specialists. Moreover, in order to provide some indications for LIS education of scientific data specialists in China, it presents the training objectives and modes.

Some job portals for librarians and the comprehensive job portals are investigated as information sources and the keywords such as “scientific data management”, “data service”, “data curation”, “e-Science”, “e-Research”, “data specialist” are selected to retrieval library-released job advertisements for scientific data specialists to understand the library's requirements towards scientific data specialists' core capabilities. Meanwhile the course catalogues of all iSchools' web sites are searched directly in order to find if scientific data courses are provided.

Libraries value teamwork ability, communication ability, interpersonal ability and a good use of data curation tools as the core competences for scientific data specialists. Candidates who possess a second advanced degree, who understand libraries, who hold demonstrated knowledge of metadata standards, and who emphasize details, under the same condition, are more likely to be considered first. Libraries do not have a unified title for scientific data specialists yet. The current curriculums of iSchools mainly cover research method, data science, data management and data service, data statistic and analysis, data warehouse, information studies and technologies, and so on.

This unique study explores some required qualifications of science data specialist surveyed by job openings, including the core skills, position requirements, responsibilities of the job, and some qualifications. It also investigates the related curriculum setting of iSchool universities through course descriptions. This study is very useful for curriculum development in Chinese LIS education of scientific data specialists including required core courses and selected electives, and to promote the practice of data service in Chinese academic libraries.

This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.

First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.

The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.

As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.

The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.

This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

The main aim of this paper is to develop a CRIS systems search profile that would enable CRIS users to perform unified and semantically rich search for the records stored in the CRIS systems.

Prior to the search profile construction, diverse representative types of the scientific research data store systems (CRISs, digital libraries, institutional repositories, and search portals) were analyzed versus available search modes, indexes and query types.

The new SRU/W standard based search profile (CRIS profile) for the purpose of searching scientific research data was proposed, that supports search for all types of data identified through an exhaustive analysis covering all major scientific and research data store systems.

Constraints of the proposed profile could appear from the fact that data identified in analyzed systems do not comprise all scientific research data recognized by CERIF standard which, in turn, could call for the profile extension.

Search profile has been verified on the data in the existing CRIS systems at the University of Novi Sad. The CRIS search profile enables unified and semantically rich search for the data stored in heterogeneous distributed scientific research data store systems.

The new SRU/W-based search profile extensively supports the search domain of scientific research data in CRIS systems. Commitments to SRU/W and CQL standards enable interoperability among heterogeneous, distributed scientific research data sources.

In recent years, a large number of data repositories have been built and used. However, the extent to which scientific data are re-used in academic publications is still unknown. The purpose of this paper is to explore the functions of re-used scientific data in scholarly publication in different fields.

To address these questions, the authors identified 827 publications citing resources in the Dryad Digital Repository indexed by Scopus from 2010 to 2015.

The results show that: the number of citations to scientific data increases sharply over the years, but mainly from data-intensive disciplines, such as agricultural, biology science, environment science and medicine; the majority of citations are from the originating articles; and researchers tend to reuse data produced by their own research groups.

Dryad data may be re-used without being formally cited.

The conservatism in data sharing suggests that more should be done to encourage researchers to re-use other’s data.

The purpose of this paper is to conduct performance evaluation of eight main scientific data sharing platforms in China and find existing problems, thus providing reference for maximizing the value of scientific data and enhancing scientific research efficiency.

First, the authors built an evaluation indicator system for the performance of scientific data sharing platforms. Next, the analytic hierarchy process was employed to set indicator weights. Then, the authors use experts grading method to give scored for each indicator and calculated the scoring results of the scientific data sharing platform performance evaluation. Finally, an analysis of the results was conducted.

The performance evaluation of eight platforms is arranged by descending order by the value of F: the Data Sharing Infrastructure of Earth System Science (76.962), the Basic Science Data Sharing Center (76.595), the National Scientific Data Sharing Platform for Population and Health (71.577), the China Earthquake Data Center (66.296), the China Meteorological Data Sharing Service System (65.159), the National Agricultural Scientific Data Sharing Center (55.068), the Chinese Forestry Science Data Center (56.894) and the National Scientific Data Sharing & Service Network on Material Environmental Corrosion (Aging) (52.528). And some existing shortcomings such as the relevant policies and regulation, standards of data description and organization, data availability and the services should be improved.

This paper is mainly discussing about the performance evaluation system covering operation management, data resource, platform function, service efficiency and influence of eight scientific data sharing centers and made comparative analysis. It reflected the reality development of scientific data sharing in China.

The “collaboratory” concept has recently entered the vernacular of the scientific community to reflect new modes of scientific communication, cooperation and collaboration made possible by information technology. The collaboratory represents a scientific research center “without walls” for accessing and sharing data, information, instrumentation and computational resources. The principal applications of the collaboratory concept have been in the physical and biological sciences, including space physics, oceanography and molecular biology. Discusses the attributes of the collaboratory, and applies the concept developed by computer and physical scientists to the design and operation of the SIPPACCESS prototype information system for complex data to be used through the Internet by sociologists, demographers and economists. Examines obstacles to collaboratory development for the social sciences. Concludes that four major obstacles will inhibit the development of collaboratories in the social sciences.

To rescue at-risk historical scientific data stored at the McGill Observatory, the objectives of the Data Rescue Archive Weather (DRAW) project are: to build a repository; to develop a protocol to preserve the data in weather registers; and to make the data available to research communities and the public. The paper aims to discuss these issues.

The DRAW project adopts an open archive information system compliant model as a conceptual framework for building a digital repository. The model consists of data collection, conversion, data capture, transcription, arrangement, description, data extraction, database design and repository setup.

A climate data repository, as the final product, is set up for digital images of registers and a database is designed for data storage. The repository provides dissemination of and access to the data for researchers, information professionals and the public.

Doing a quality check is the most important aspect of rescuing historical scientific data to ensure the accuracy, reliability and consistency of data.

The DRAW project shows how the use of historical scientific data has become a key element in research analysis on scientific fields, such as climatology and environmental protection.

The historical climate data set of the McGill Observatory is by nature unique and complex for preservation and research purposes. The management of historical scientific data is a challenge to rescue and describe as a result of its heterogeneous and non-standardized form.

This study aims to investigate the preferences of 96 educational researchers on the use of digital technologies in scientific research.

The study was designed as a quantitative-dominant sequential explanatory mixed-method research.

Despite the spreading use of advanced technologies of big data and data mining, the most preferred digital technologies were found to be data analysis programs, databases and questionnaires. The primary reasons of using digital technology in scientific research were to collect data easily and quickly, to reduce research costs and to reach a higher number of participants.

The use of digital technologies in scientific research is considered a revolutionary action, which creates innovative opportunities. Through digitalized life, probably for the first time in history, the educational researchers have analytical information, which we can benefit from more than the individual's own statements in research involving human factor. However, there are a few studies that investigated the preferences of educational researchers who use digital technologies in their scientific research.

Recent technological advances have created volumes of data such that, unless some effective methods are used to analyse them, they will be either wasted or under‐examined for their useful information content. Scientific data visualization is an attempt to use graphical and numerical tools to extract information contained in data and hence to allow its analysis. This paper seeks to present a systematic approach to the development of tools for scientific data visualization.

It is shown that the approach to implement these tools involves four major steps: description of a reference model, validation of the data process, description of the software component and the design and implementation of the visualization tool.

This approach is substantiated by defining conditions suitable for scientific data visualization processes, in a relaxed manner. These conditions are subsequently refined more formally. Definitions and theorems of the proofs are succinctly discussed.

The mathematical description of the visualization process is necessary to understand and maintain some significant reduction in errors in scientific visualization processes.

This paper aims to analyse a set of converging trends underpinning a larger phenomenon called science 2.0 and to assess what are the most important implications for scientific method and research institutions.

It is based on a triangulation of exploratory methods which include a wide-ranging literature review, Web-based mapping and in-depth interviews with stakeholders.

The main implications of science 2.0 are enhanced efficiency, transparency and reliability; raise of data-driven science; microcontributions on a macroscale; multidimensional, immediate and multiform evaluation of science; disaggregation of the value chain of service providers for scientists; influx of multiple actors and the democratisation of science.

The paper rejects the notion of science 2.0 as the mere adoption of Web 2.0 technologies in science and puts forward an original integrated definition covering three trends that have not yet been analysed together: open science, citizens science and data-intensive science. It argues that these trends are mutually reinforcing and puts forward their main implications. It concludes with the identification of three enablers of science 2.0 – policy measures, individual practice of scientists and new infrastructure and services and sees the main bottleneck in lack of incentives on the individual level.